CN112861378B - Improved Husky algorithm-based boosting section flight program optimization method and device - Google Patents
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Abstract
The application relates to a boosting section flight program optimization method and device based on an improved wolf algorithm, computer equipment and a storage medium. The method comprises the following steps: constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section, taking an attack angle characteristic parameter as a position vector of the gray wolf, and initializing a gray wolf population; the method comprises the steps of improving a linear convergence factor in a classical grey wolf algorithm into a nonlinear convergence factor with slower attenuation in the early stage of iteration and faster attenuation in the later stage of iteration, optimizing attack angle characteristic parameters through the improved grey wolf algorithm, updating a position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, and obtaining a standard trajectory of a trajectory missile motion model according to the position vector of the optimal grey wolf. According to the method, the key parameters of the attack angle model are used as optimization variables, the minimum terminal state deviation is used as an optimization target, the standard trajectory meeting the constraint is iteratively optimized, and the generation precision of the standard trajectory of the boosting section of the solid missile is further improved.
Description
Technical Field
The application relates to the technical field of ballistic planning, in particular to a boosting section flight program optimization method and device based on an improved wolf algorithm, computer equipment and a storage medium.
Background
The ballistic missile has the outstanding characteristics of long range, high precision, large power, strong destructive power and the like, is a weapon with extremely strong aggressiveness and deterrence, and is also an important support for protecting the safety of the national soil and maintaining the strategic balance of the region. The flight trajectory of a ballistic missile is mostly bound off-line, so that the design of a standard ballistic missile which meets the battlefield requirements and can complete a specified task has very important significance.
The prior art has the problem that the ballistic design precision is not high enough.
Disclosure of Invention
In view of the above, there is a need to provide a method, an apparatus, a computer device and a storage medium for optimizing a boosted flight program based on an improved grayling algorithm, which can improve the accuracy of ballistic design.
A method of boosting segment flight program optimization based on an improved grayling algorithm, the method comprising:
constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section;
taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range;
improving a linear convergence factor in a classical grey wolf algorithm into a nonlinear convergence factor to obtain an improved grey wolf algorithm; the nonlinear convergence factor is smaller in attenuation speed at the moment before iteration than in attenuation speed at the moment after iteration;
and optimizing the attack angle characteristic parameters through the improved grey wolf algorithm, updating the position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting the globally optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf.
In one embodiment, the method further comprises the following steps: the method comprises the following steps of (1) constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section:
wherein α (t) represents the angle of attack at time t; t is t 0 ,t 11 Representing the start-stop time of the vertical takeoff segment; t is t 12 ,t 13 Representing the start-stop time of the transonic segment; t is t 1f ,t 20 ,t 2f ,t 30 ,t 3f Representing the start and stop times of the interstage separation section; t is t 20 ,t 2f Representing the starting and stopping time of the second-stage flight section of the missile; t is t 30 ,t f Representing the starting and stopping time of the third-level flight section of the missile; the indicated start-stop time;
t m denotes alpha 1 Corresponding time; alpha (alpha) ("alpha") 1 、α 2 、α 3 、α 4 The minimum value of the first to fourth negative angle of attack turns, respectively.
In one embodiment, the method further comprises the following steps: taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm; the attack angle characteristic parameter is the minimum value of four negative attack angle turns;
generating a random value in a preset value range, and initializing the wolf population according to the random value.
In one embodiment, the method further comprises the following steps: improving a linear convergence factor in a classical grey wolf algorithm into a nonlinear convergence factor to obtain an improved grey wolf algorithm; the nonlinear convergence factor is:
wherein a is a nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum number of iterations.
In one embodiment, the method further comprises the following steps: obtaining the current terminal state of the missile according to the attack angle characteristic parameters; the terminal state of the missile comprises the terminal height, the speed and the trajectory inclination angle of the missile.
In one embodiment, the method further comprises the following steps: acquiring a preset target terminal state;
according to the position vector of each wolf in the current wolf population, obtaining the current terminal state corresponding to the position vector;
obtaining a terminal state deviation according to the target terminal state and the current terminal state;
finding a position vector which enables the fitness function of the terminal state deviation to be minimum, and taking the position vector as an optimization result;
and updating the position vector of the wolf population according to the optimizing result.
In one embodiment, the method further comprises the following steps: optimizing the attack angle characteristic parameters through the improved wolf algorithm, updating the position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, and outputting a global optimal wolf; the fitness function of the terminal state deviation is as follows:
wherein Fitness represents a Fitness function value of the terminal state deviation; h f 、V f 、θ f Representing the height, the speed and the trajectory inclination angle of the target terminal; h present 、V present 、θ present Representing the current terminal height, speed and trajectory inclination angle obtained after each optimization calculation; Δ H, Δ V, Δ θ represent the maximum values of allowable deviation of altitude, velocity, and ballistic inclination angles set in advance.
A boosted flight procedure optimization apparatus based on an improved grayling algorithm, the apparatus comprising:
the attack angle model determining module is used for constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section;
the grey wolf population initialization module is used for taking the attack angle characteristic parameters in the attack angle model as the position vectors of the grey wolfs in the grey wolf algorithm and initializing the grey wolf population according to a preset value range;
the convergence factor improving module is used for improving a linear convergence factor in the classic grayish wolf algorithm into a nonlinear convergence factor to obtain an improved grayish wolf algorithm; the nonlinear convergence factor is smaller in attenuation speed at the moment before iteration than in attenuation speed at the moment after iteration;
and the iteration module is used for optimizing the attack angle characteristic parameters through the improved grey wolf algorithm, updating the position vector according to a fitness function of the terminal state deviation, iterating until the maximum iteration times are reached, outputting the global optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section;
taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range;
improving a linear convergence factor in a classical grey wolf algorithm into a nonlinear convergence factor to obtain an improved grey wolf algorithm; the nonlinear convergence factor is attenuated slowly in the early stage of iteration and is attenuated quickly in the later stage of iteration;
and optimizing the attack angle characteristic parameters through the improved grey wolf algorithm, updating the position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting the globally optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section;
taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range;
improving a linear convergence factor in the classic grayish wolf algorithm into a nonlinear convergence factor to obtain an improved grayish wolf algorithm; the nonlinear convergence factor is smaller in attenuation speed at the moment before iteration than in attenuation speed at the moment after iteration;
and optimizing the attack angle characteristic parameters through the improved grey wolf algorithm, updating the position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting the globally optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf.
According to the boosting section flight program optimization method, device, computer equipment and storage medium based on the improved wolf algorithm, a ballistic missile motion model is constructed, and an attack angle model of a missile boosting section is obtained; taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range; the linear convergence factor in the classical grey wolf algorithm is improved into a nonlinear convergence factor which is slowly attenuated in the early stage of iteration and is quickly attenuated in the later stage of iteration, so that the improved grey wolf algorithm is obtained; and optimizing the characteristic parameters of the attack angle through an improved grey wolf algorithm, updating a position vector according to a fitness function of the terminal state deviation, iterating until the maximum iteration times are reached, outputting the global optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf. The method takes the key parameters of the attack angle model as optimization variables and the minimum terminal state deviation as an optimization target, iteratively optimizes the standard trajectory meeting the constraint, and further improves the generation precision of the standard trajectory of the boosting section of the solid missile.
Drawings
FIG. 1 is a flow diagram illustrating a method for improving a boosted flight program based on the improved Grey wolf algorithm according to one embodiment;
FIG. 2 is a schematic view of a pitch program angle in one embodiment;
FIG. 3 is a schematic illustration of a variation of angle of attack in one embodiment;
FIG. 4 is a graph comparing the convergence factor of the classic Grey wolf algorithm and one embodiment;
FIG. 5 is a graph of the primary ballistic parameter curve and the change in fitness value for a standard ballistic trajectory in one embodiment;
FIG. 6 is a schematic diagram of a boost phase flight program optimization device based on the improved Grey wolf algorithm in one embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The boosting section flight program optimization method based on the improved wolf algorithm can be applied to the following application environments. Constructing a trajectory missile motion model and obtaining an attack angle model of a missile boosting section; taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range; the linear convergence factor in the classical grey wolf algorithm is improved into a nonlinear convergence factor which is attenuated slowly in the early stage of iteration and is attenuated quickly in the later stage of iteration, and an improved grey wolf algorithm is obtained; optimizing the attack angle characteristic parameters through an improved grey wolf algorithm, updating a position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting a global optimal grey wolf, and obtaining a standard trajectory of a trajectory missile motion model according to the position vector of the optimal grey wolf.
In one embodiment, as shown in fig. 1, there is provided a method for optimizing a boosted flight program based on an improved gray wolf algorithm, comprising the steps of:
and 102, constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section.
In the invention, the earth is regarded as a homogeneous sphere, the influence of control force is ignored, and the zero-inclination flight of the axisymmetric missile is considered, so that a simplified model of a ballistic missile motion model is obtained as follows:
wherein theta is a trajectory inclination angle, upsilon is an inclination angle, alpha is an attack angle, and beta is a sideslip angle.
Is the rate of change of height;
is the rate of change of geocentric distance;
is the rate of change of speed;
is the rate of change of ballistic dip;
is the second consumption; p is thrust along the direction of the projectile body; m is mass; g is gravitational acceleration; d is resistance; l is a lifting force; r is the distance between centers of earth.
According to the missile boosting section motion law, the time variation law of the pitch angle is designed as shown in figure 2,
representing the pitch program angle. Wherein, t 0 -t 11 For vertical takeoff section, t 12 -t 13 In the transonic speed section, in order to control the height of the missile terminal, a secondary turning mode is selected and adopted in the first stage. t is t 1f -t 20 、t 2f -t 30 As interstage separation section, t 20 -t 2f For the second flight phase of the missile, t 30 -t f Is the third-level flight section of the missile. To realize the pitch flight procedure angle in the above figure, the variation form of the angle of attack satisfying the terminal condition is designed as shown in fig. 3, where α represents the angle of attack.
The change rule of the attack angle is determined by solving the key parameters of the attack angle model, and then the change rule of the pitch angle is obtained by utilizing the Euler angle transformation relation, so that the problem of solving the standard trajectory is converted into the parameter optimization problem.
And 104, taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range.
The grey wolf algorithm is a group intelligent algorithm which is a novel group intelligent algorithm proposed by scholars such as Seyedali in 2014, the grey wolf algorithm simulates a grey wolf level system and hunting behaviors in nature, the whole grey wolf group is divided into four groups of alpha, beta, delta and omega, the first three groups are three groups with the best fitness in sequence, and the three groups guide the wolf of the fourth group to think of target search, and the wolf group updates the positions of alpha, beta, delta and omega in the optimization process. In the present invention, each grayish wolf is a four-dimensional vector, which represents a set of solutions of four key parameters, wherein each parameter is the four attack angle characteristic values shown in fig. 3, and the initial value of the position of the grayish wolf is a random value generated in the corresponding search space range.
In the classical grey wolf algorithm, the updating mode of the grey wolf individuals is as follows:
X(t+1)＝X(t)-A·D
wherein X represents a location vector of the wolf, t represents a current iteration number, D represents a distance between the individual and the target, A represents a control search field, A =2a · r 2 -a，r 2 Is [0,1]Random numbers within the range, a representing an iteration factor,
the convergence factor a is the current decrease from 2 to 0 with the number of iterations. When | A | > 1, the wolf group expands the search scope, corresponding to global search, and when | A | < 1, the wolf group will shrink the enclosure, corresponding to local search.
The convergence factor a of the classical grayish wolf algorithm is linearly reduced from 2 to 0, and the rate of developing global search in the early stage is consistent with the rate of developing local search in the later stage, which reduces the search efficiency to a certain extent. In order to improve the algorithm efficiency when the wolf algorithm is applied to the scene of the invention, the invention provides a nonlinear convergence factor which is attenuated slowly in the early stage of iteration and is attenuated quickly in the later stage of iteration, and the expression is as follows:
the change of the convergence factor of the classic grayish wolf algorithm and the improved grayish wolf algorithm along with the iteration times is shown in fig. 4, and it can be seen that the slope of the improved nonlinear convergence factor is lower than that of the iteration factor of the classic grayish wolf algorithm in the initial iteration stage, the search speed is slow, the optimal region can be searched in more sufficient time, and in the later iteration stage, the slope is rapidly increased, which means that when the optimized direction is found, the search speed is increased, and the optimal solution is found quickly.
And 108, optimizing the characteristic parameters of the attack angle through an improved grey wolf algorithm, updating a position vector according to a fitness function of the terminal state deviation, iterating until the maximum iteration times are reached, outputting the globally optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf.
And (3) finding a position vector corresponding to the minimum value of the deviation of the terminal state of each iteration, namely four attack angle characteristic values of the attack angle model, by calculating the deviation of the terminal state, and updating the alpha, beta and delta values in the cycle. As the iterations accumulate, the grey wolf positions of each level will gradually approach the target, and the optimal fitness value will also be smaller and smaller. In an ideal state, the optimal fitness value tends to zero through continuous iterative calculation. And then finding out an optimal solution meeting the requirements, and replacing a rebound channel missile motion model to obtain a corresponding standard trajectory according to the optimal solution of the four attack angle characteristic values.
In the boosting section flight program optimization method based on the improved gray wolf algorithm, a ballistic missile motion model is constructed, and an attack angle model of a missile boosting section is obtained; taking an attack angle characteristic parameter in the attack angle model as a gray wolf position vector in a gray wolf algorithm, and initializing a gray wolf population according to a preset value range; the linear convergence factor in the classical grey wolf algorithm is improved into a nonlinear convergence factor which is slowly attenuated in the early stage of iteration and is quickly attenuated in the later stage of iteration, so that the improved grey wolf algorithm is obtained; optimizing the attack angle characteristic parameters through an improved grey wolf algorithm, updating a position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting a global optimal grey wolf, and obtaining a standard trajectory of a trajectory missile motion model according to the position vector of the optimal grey wolf. According to the method, the key parameters of the attack angle model are used as optimization variables, the minimum terminal state deviation is used as an optimization target, the standard trajectory meeting the constraint is iteratively optimized, and the generation precision of the standard trajectory of the boosting section of the solid missile is further improved.
In one embodiment, the method further comprises the following steps: the method comprises the following steps of (1) constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section:
wherein α (t) represents the angle of attack at time t; t is t 0 ,t 11 Representing the start-stop time of the vertical takeoff segment; t is t 12 ,t 13 Representing the start-stop time of the transonic segment; t is t 1f ,t 20 ,t 2f ,t 30 ,t 3f Representing the start and stop times of the interstage separation section; t is t 20 ,t 2f Representing the starting and stopping time of the second-stage flight section of the missile; t is t 30 ,t f Representing the starting and stopping time of the third-level flight section of the missile; the indicated start-stop time;
t m denotes alpha 1 Corresponding time; alpha is alpha 1 、α 2 、α 3 、α 4 The minimum value of the first to fourth negative angle of attack turns, respectively.
In one embodiment, the method further comprises the following steps: taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm; the characteristic parameter of the attack angle is the minimum value of four times of negative attack angle turning; and generating a random value within a preset value range, and initializing the wolf population according to the random value.
In one embodiment, the method further comprises the following steps: improving a linear convergence factor in a classical grey wolf algorithm into a nonlinear convergence factor to obtain an improved grey wolf algorithm; the nonlinear convergence factor is:
wherein a is a nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum number of iterations.
The nonlinear convergence factor in the embodiment has the characteristics that the slope change range is large, the slope value is small in the early stage of iteration, sufficient time is reserved for global search, the slope value is rapidly increased in the later stage of iteration, the speed of local search is increased, and the requirement of the flight program for optimizing the scene is met.
In one embodiment, the method further comprises the following steps: obtaining the current terminal state of the missile according to the characteristic parameters of the attack angle; the missile terminal state comprises the terminal height, the speed and the trajectory inclination angle of the missile.
In one embodiment, the method further comprises the following steps: acquiring a preset target terminal state; obtaining a current terminal state corresponding to the position vector according to the position vector of each wolf in the current wolf population; the terminal state of the missile comprises the terminal height, the speed and the trajectory inclination angle of the missile; obtaining a terminal state deviation according to the target terminal state and the current terminal state; finding a position vector which enables the fitness function of the terminal state deviation to be minimum, and taking the position vector as an optimization result; the fitness function of the terminal state deviation is as follows:
wherein, fitness represents a Fitness function value of the terminal state deviation; h f 、V f 、θ f Representing the height, the speed and the trajectory inclination angle of the target terminal; h present 、V present 、θ present Representing the current terminal height, speed and trajectory inclination angle obtained after each optimization calculation; Δ H, Δ V, Δ θ represent the maximum values of allowable deviation of altitude, velocity, and ballistic inclination angles set in advance.
Updating the position vector of the wolf population according to the optimizing result; and iterating until the maximum iteration times is reached, and outputting the global optimal grayish wolf.
In one embodiment, the designed initial trajectory has a terminal height h f =90km, terminal ballistic inclination angle θ f =0 °, terminal speed of
Wherein,
is a given speed value used to normalize the decryption process. In the solving process, in order to ensure the calculation accuracy, the number of the gray wolves is not set too small, on the other hand, in consideration of improving the calculation efficiency as much as possible, the number of the gray wolves is not set too large, and the maximum number of iterative cycles is also not set too high. Therefore, by comprehensive consideration, the population size is taken to be 20, and the maximum iteration number is 20. The initial value of the position of the gray wolf is a random value generated in the corresponding search space range, and the initial search space of the attack angle is set as shown in table 1.
TABLE 1 Angle of attack eigenvalue search space
Through iterative computation, a standard trajectory which can meet terminal constraint and process constraint is obtained, and four attack angle characteristic values obtained through simulation are shown in table 2 and are respectively alpha 1 ＝-13.3358°，α 2 ＝-6.5173°，α 3 ＝-17°，α 4 And = -20.4 degrees, and a main trajectory parameter curve and a variation curve of the fitness value of the standard trajectory obtained through simulation are shown in fig. 5, wherein the speed is normalized through simulation results.
TABLE 2 simulation results of angle of attack eigenvalues
According to simulation results, the terminal height obtained by designing an initial trajectory by utilizing a wolf algorithm is 89.84km, the terminal trajectory inclination angle is-0.1727 degrees, the terminal speed deviation is 17m/s, the terminal state deviation does not exceed the deviation index constraint, and the graph shows that the change curves of the height, the speed and the trajectory inclination angle are relatively smooth in the whole flight process of the boosting section. In addition, the change form of the attack angle is consistent with the design rule, and the process constraint also meets the given index requirement. As can be seen from fig. 5 (g), when the iteration is performed to the seventh time, the value of the optimal fitness is already smaller than 1, and at this time, the algorithm has already found an optimal solution that can satisfy the process constraint and the terminal constraint, so that it can be seen that the standard ballistic design method based on the gray wolf algorithm has better accuracy and calculation efficiency.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a boosted flight program optimization device based on an improved gray wolf algorithm, comprising: an angle of attack model determination module 602, a grey wolf population initialization module 604, a convergence factor improvement module 606, and an iteration module 608, wherein:
the attack angle model determining module 602 is configured to construct a ballistic missile motion model and obtain an attack angle model of a missile boosting section;
the grey wolf population initialization module 604 is configured to use an attack angle characteristic parameter in the attack angle model as a position vector of the grey wolf in the grey wolf algorithm, and initialize a grey wolf population according to a preset value range;
a convergence factor improving module 606, configured to improve a linear convergence factor in the classical grayish wolf algorithm into a nonlinear convergence factor, so as to obtain an improved grayish wolf algorithm; the attenuation speed of the nonlinear convergence factor at the moment before iteration is smaller than the attenuation speed at the moment after iteration;
and the iteration module 608 is configured to optimize the attack angle characteristic parameters through an improved grey wolf algorithm, update a position vector according to a fitness function of the terminal state deviation, iterate until the maximum iteration number is reached, output a global optimal grey wolf, and obtain a standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf.
The grey wolf population initialization module 604 is further configured to use the characteristic parameters of the attack angle in the attack angle model as the position vector of the grey wolf in the grey wolf algorithm; the characteristic parameter of the attack angle is the minimum value of four negative attack angle turns; and generating a random value within a preset value range, and initializing the wolf population according to the random value.
The convergence factor improving module 606 is further configured to obtain a preset target terminal state; obtaining a current terminal state corresponding to the position vector according to the position vector of each wolf in the current wolf population; obtaining a terminal state deviation according to the target terminal state and the current terminal state; finding a position vector which enables the fitness function of the terminal state deviation to be minimum, and taking the position vector as an optimizing result; and updating the position vector of the wolf population according to the optimizing result.
For specific limitations of the improved grayling algorithm based boost segment flight program optimization device, reference may be made to the above limitations of the improved grayling algorithm based boost segment flight program optimization method, and details thereof are not repeated herein. The various modules in the boost phase flight program optimization device based on the improved wolf algorithm described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of boosting phase flight program optimization based on an improved grayling algorithm. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the configuration shown in fig. 7 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (7)
1. A method for optimizing a boosted flight program based on an improved wolf algorithm, the method comprising:
constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section;
taking the characteristic parameters of the attack angle in the attack angle model as the position vector of the gray wolf in the gray wolf algorithm, and initializing a gray wolf population according to a preset value range; the characteristic parameter of the attack angle is the minimum value of four negative attack angle turns;
improving a linear convergence factor in the classic grayish wolf algorithm into a nonlinear convergence factor to obtain an improved grayish wolf algorithm; the nonlinear convergence factor is smaller in attenuation speed at the moment before iteration than in attenuation speed at the moment after iteration; the nonlinear convergence factor is:
wherein a is a nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum number of iterations;
optimizing the attack angle characteristic parameters through the improved grey wolf algorithm, updating the position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting a global optimal grey wolf, and obtaining a standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf; the fitness function of the terminal state deviation is as follows:
wherein, fitness represents a Fitness function value of the terminal state deviation; h f 、V f 、θ f Representing target terminal height, velocity, and ballistic dip; h present 、V present 、θ present Representing the current terminal height, speed and trajectory inclination angle obtained after each optimization calculation; Δ H, Δ V, Δ θ represent the maximum values of allowable deviation of the altitude, velocity, and ballistic inclination angles set in advance.
2. The method of claim 1, wherein constructing a ballistic missile motion model and deriving an angle of attack model for a missile thrust segment comprises:
the method comprises the following steps of (1) constructing a trajectory missile motion model and obtaining an attack angle model of a missile boosting section:
wherein α (t) represents the angle of attack at time t; t is t 0 ,t 11 Representing the start-stop time of the vertical takeoff segment; t is t 12 ,t 13 Representing the start-stop time of the transonic speed section; t is t 1f ,t 20 ,t 2f ,t 30 ,t 3f Representing the start and stop times of the interstage separation section; t is t 20 ,t 2f Representing the starting and stopping time of the second-stage flight section of the missile; t is t 30 ,t 3f Representing the starting and stopping time of the third-level flight section of the missile;
t m denotes alpha 1 Corresponding time; alpha is alpha 1 、α 2 、α 3 、α 4 The minimum value of the first to fourth negative angle of attack turns, respectively.
3. The method of claim 2, wherein before updating the location vector according to the fitness function of the terminal state deviation, further comprising:
obtaining the current missile terminal state according to the attack angle characteristic parameters; the missile terminal state comprises the terminal height, the speed and the trajectory inclination angle of the missile.
4. The method of claim 3, wherein the step of optimizing the angle of attack feature parameters by the modified grayish wolf algorithm and updating the position vector according to a fitness function of the terminal state deviation comprises:
acquiring a preset target terminal state;
according to the position vector of each wolf in the current wolf population, obtaining the current terminal state corresponding to the position vector;
obtaining a terminal state deviation according to the target terminal state and the current terminal state;
finding a position vector which enables the fitness function of the terminal state deviation to be minimum, and taking the position vector as an optimization result;
and updating the position vector of the wolf population according to the optimizing result.
5. A boost segment flight program optimization apparatus based on an improved grayling algorithm, the apparatus comprising:
the attack angle model determining module is used for constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section;
the grey wolf population initialization module is used for taking the attack angle characteristic parameters in the attack angle model as the position vectors of the grey wolfs in the grey wolf algorithm and initializing the grey wolf population according to a preset value range; the attack angle characteristic parameter is the minimum value of four negative attack angle turns;
the convergence factor improving module is used for improving the linear convergence factor in the classic grayling algorithm into a nonlinear convergence factor to obtain an improved grayling algorithm; the nonlinear convergence factor is smaller in attenuation speed at the moment before iteration than in attenuation speed at the moment after iteration; the nonlinear convergence factor is:
wherein a is a nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum number of iterations;
the iteration module is used for optimizing the attack angle characteristic parameters through the improved grey wolf algorithm, updating the position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, outputting the globally optimal grey wolf, and obtaining the standard trajectory of the trajectory missile motion model according to the position vector of the optimal grey wolf; the fitness function of the terminal state deviation is as follows:
wherein, fitness represents a Fitness function value of the terminal state deviation; h f 、V f 、θ f To show the eyesMarking the terminal height, speed and trajectory inclination angle; h present 、V present 、θ present Representing the current terminal height, speed and trajectory inclination angle obtained after each optimization calculation; Δ H, Δ V, Δ θ represent the maximum values of allowable deviation of altitude, velocity, and ballistic inclination angles set in advance.
6. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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